qntz
Vector quantization primitives for approximate nearest neighbor search: RaBitQ (1-8 bit), ternary quantization, bit packing, and Hamming distance. Backed by a Rust implementation.
Python bindings for the qntz Rust crate.
Install
pip install qntz
Quick start
import qntz
# RaBitQ quantization (1-8 bits per dimension)
dim = 32
quantizer = qntz.RaBitQQuantizer(dim, seed=42, total_bits=4)
vector = [float(i) * 0.1 for i in range(dim)]
qv = quantizer.quantize(vector)
query = [float(i) * 0.05 for i in range(dim)]
dist = quantizer.approximate_distance(query, qv)
# Ternary quantization ({-1, 0, +1} per dimension)
tq = qntz.TernaryQuantizer(4, threshold_high=0.3, threshold_low=-0.3, normalize=False)
a = tq.quantize([0.5, -0.5, 0.1, -0.1])
b = tq.quantize([0.5, 0.5, 0.0, -0.5])
sim = qntz.ternary_cosine_similarity(a, b)
# Bit packing and Hamming distance
packed_a = qntz.pack_binary([1, 0, 1, 0, 1, 0, 1, 0])
packed_b = qntz.pack_binary([1, 1, 1, 0, 0, 0, 1, 0])
dist = qntz.hamming_distance(packed_a, packed_b) # 2
API
| Name | Description |
|---|---|
RaBitQQuantizer |
Quantizer using randomized Hadamard rotation, 1-8 bits/dim |
RaBitQQuantizer.fit |
Fit centroid from training vectors |
RaBitQQuantizer.quantize |
Quantize a single vector, returns QuantizedVector |
RaBitQQuantizer.approximate_distance |
Approximate Euclidean distance to a quantized vector |
RaBitQQuantizer.approximate_l2_sqr |
Approximate squared L2 distance to a quantized vector |
TernaryQuantizer |
Maps each dimension to {-1, 0, +1} via thresholds |
TernaryQuantizer.fit |
Fit adaptive thresholds from training vectors |
TernaryQuantizer.quantize |
Quantize a single vector, returns TernaryVector |
pack_binary |
Pack 0/1 values into a compact bitfield |
unpack_binary |
Unpack a bitfield back to one byte per bit |
hamming_distance |
Hamming distance between two packed bit-vectors |
asymmetric_inner_product |
f32 query vs packed 1-bit codes inner product |
asymmetric_l2_squared |
f32 query vs packed 1-bit codes L2 squared |
ternary_inner_product |
Inner product between two ternary vectors |
ternary_cosine_similarity |
Cosine similarity between two ternary vectors |
ternary_hamming |
Hamming distance between two ternary vectors |
ternary_asymmetric_inner_product |
f32 query vs ternary codes inner product |
ternary_asymmetric_cosine_distance |
f32 query vs ternary codes cosine distance |
numpy support
All functions accept numpy arrays or Python lists. Packed outputs are numpy uint8 arrays.
License
MIT OR Apache-2.0
1 maintainer
Release files for qntz 0.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
Total release size: 9.7 MB
Release files / qntz-0.1.3-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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